{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f8436be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # 导入评估指标函数\n",
    "import matplotlib.pyplot as plt  # 导入数据可视化库 Matplotlib\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62504e3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd625d5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b212ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale train and test data to new feature range[0, 1]将训练和测试数据缩放到新的特征范围[0，1]\n",
    "def scale(train, test):\n",
    "\t# fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n",
    "\n",
    "def scaled(X, y):\n",
    "    max_train = np.max(X, axis=0) #标准化\n",
    "    min_train = np.min(X, axis=0)\n",
    "    X = 0.0 + (X - min_train) / (max_train - min_train)\n",
    "    max_targets = np.max(y, axis=0)\n",
    "    min_targets = np.min(y, axis=0)\n",
    "    y = 0.0 + (y - min_targets) / (max_targets - min_targets)\n",
    "    return X, y, max_train, min_train, max_targets, min_targets\n",
    "\n",
    "def inscaled(ypred):\n",
    "    y_pred = ypred * (max_targets - min_targets) + min_targets\n",
    "    return y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74b5554c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       radiation  air temperature  air pressure  humidity  actual power/MW\n",
      "0            0.0            -14.1       908.672    81.997              0.0\n",
      "1            0.0            -14.6       908.672    82.597              0.0\n",
      "2            0.0            -14.4       908.672    82.897              0.0\n",
      "3            0.0            -14.8       908.672    82.997              0.0\n",
      "4            0.0            -14.8       909.172    82.897              0.0\n",
      "...          ...              ...           ...       ...              ...\n",
      "35021        0.0             -4.8       899.600    54.900              0.0\n",
      "35022        0.0             -5.9       900.100    55.800              0.0\n",
      "35023        0.0             -6.5       900.100    57.900              0.0\n",
      "35024        0.0             -7.4       900.100    59.400              0.0\n",
      "35025        0.0             -8.0       900.100    62.700              0.0\n",
      "\n",
      "[35026 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('PVDATA.csv')\n",
    "# 删除时间列\n",
    "df = df.drop(columns=['TIME'])\n",
    "# 目标列\n",
    "target_column = 'actual power/MW'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dc78b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.    -14.1   908.672  81.997]\n",
      " [  0.    -14.6   908.672  82.597]\n",
      " [  0.    -14.4   908.672  82.897]\n",
      " ...\n",
      " [  0.     -6.5   900.1    57.9  ]\n",
      " [  0.     -7.4   900.1    59.4  ]\n",
      " [  0.     -8.    900.1    62.7  ]]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=target_column).values  # 从 DataFrame `df` 中去除目标列 `target_column`，并将剩余的列转换为数组赋值给变量 `X`\n",
    "\n",
    "y = df[target_column].values  # 从 DataFrame `df` 中选择目标列 `target_column`，并将其转换为数组赋值给变量 `y`\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "079a903b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.70982043 -0.1581825  -0.25278188  0.63564631]\n",
      " [ 0.70982043  1.         -0.59307447 -0.14555079  0.57492931]\n",
      " [-0.1581825  -0.59307447  1.         -0.0128098  -0.2217157 ]\n",
      " [-0.25278188 -0.14555079 -0.0128098   1.         -0.29023335]\n",
      " [ 0.63564631  0.57492931 -0.2217157  -0.29023335  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "X, y, max_train, min_train, max_targets, min_targets = scaled(X, y)#标准化\n",
    "# 计算皮尔逊相关系数矩阵\n",
    "corr = np.corrcoef(X,y,rowvar=False)\n",
    "print(corr)\n",
    "# 计算每个特征的相关性权重\n",
    "weights = np.abs(corr[-1, :-1])\n",
    "# 对数据进行加权\n",
    "X = np.multiply(X, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfa2bf71",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [ 'radiation','air temperature', 'air pressure','humidity','actual power/MW']\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={\"size\": 12},\n",
    "            xticklabels=feature_names, yticklabels=feature_names,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfef9bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28020,)\n"
     ]
    }
   ],
   "source": [
    "dataset = y\n",
    "train_size = int(len(dataset) * 0.8)  # 计算训练集大小，将数据集长度的 80% 转换为整数\n",
    "test_size = len(dataset) - train_size  # 计算测试集大小，将剩余的数据作为测试集\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]  # 将数据集划分为训练集和测试集，使用切片操作实现\n",
    "print(\"Train X shape:\", train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e3de8f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4e60c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''定义一个函数用于创建数据集，输入参数包括:\n",
    "dataset: 数据集\n",
    "look_back: 回溯长度，即用多少个时间步作为输入预测下一个时间步'''\n",
    "def creat_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):# 循环遍历数据集\n",
    "        a = dataset[i: (i + look_back)]# 提取回溯长度的数据作为输入\n",
    "        dataX.append(a)# 将该输入数据添加到输入数据集\n",
    "        dataY.append(dataset[i + look_back])# 将下一个时间步的数据添加到输出数据集\n",
    "    return np.array(dataX), np.array(dataY)# 将输入输出数据集转换为numpy数组并返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e15085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 2\n",
    "train_X, train_Y = creat_dataset(train, look_back)  # 根据训练集数据和滑动窗口大小创建训练集的输入序列和输出值\n",
    "test_X, test_Y = creat_dataset(test, look_back)  # 根据测试集数据和滑动窗口大小创建测试集的输入序列和输出值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc162762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28017, 2)\n",
      "Train Y shape: (28017,)\n",
      "Test X shape: (7003, 2)\n",
      "Test Y shape: (7003,)\n"
     ]
    }
   ],
   "source": [
    "# Ensure the sizes of training and testing sets\n",
    "print(\"Train X shape:\", train_X.shape)\n",
    "print(\"Train Y shape:\", train_Y.shape)\n",
    "print(\"Test X shape:\", test_X.shape)\n",
    "print(\"Test Y shape:\", test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fac2c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调用GPU加速\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "482a57a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
    "\n",
    "save_best = ModelCheckpoint(\"new_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67b01cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "876/876 [==============================] - 11s 9ms/step - loss: 0.0068 - val_loss: 0.0033 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0049 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0048 - val_loss: 0.0027 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0047 - val_loss: 0.0027 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0047 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0046 - val_loss: 0.0032 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "875/876 [============================>.] - ETA: 0s - loss: 0.0046\n",
      "Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0046 - val_loss: 0.0027 - lr: 0.0010\n",
      "Epoch 9/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0045 - val_loss: 0.0025 - lr: 2.5000e-04\n",
      "Epoch 10/10\n",
      "876/876 [==============================] - 7s 8ms/step - loss: 0.0044 - val_loss: 0.0025 - lr: 2.5000e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e0f7f6a850>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.layers import Concatenate, Input\n",
    "from keras.models import Model\n",
    "from tcn import TCN, tcn_full_summary \n",
    "from tensorflow.keras.layers import Conv1D, LSTM, Bidirectional ,ZeroPadding1D ,BatchNormalization, Add,Layer,Dot\n",
    "\n",
    "input1 = Input(shape=(train_X.shape[1], 1))\n",
    "x = Input(shape=(train_X.shape[1], 1))\n",
    "max_len = 200\n",
    "rnn_cell_size = 128\n",
    "x1 = Bidirectional(LSTM(rnn_cell_size,\n",
    "                        return_sequences=True), name=\"bi_lstm_0\")(x)\n",
    "x2= Dropout(0.20)(x1)\n",
    "\n",
    "x4 = TCN(return_sequences=False)(x2)\n",
    "output = Dense(1, activation='sigmoid')(x4)\n",
    "model = Model(inputs=x, outputs=output)\n",
    "# Compile and train the CNN model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(train_X, train_Y, epochs=10, batch_size=32,validation_data=(test_X , test_Y),\n",
    "            callbacks=[reduce_lr , save_best])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cdd2fb0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "876/876 [==============================] - 3s 3ms/step\n",
      "219/219 [==============================] - 1s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "# 对训练集进行预测\n",
    "ypred_train = model.predict(train_X)\n",
    "# 将训练集的预测结果转换为原始数据的范围\n",
    "ypred_train = inscaled(ypred_train)\n",
    "# 将训练集的真实标签转换为原始数据的范围\n",
    "y_train = inscaled(train_Y)\n",
    "\n",
    "# 对测试集进行预测\n",
    "ypred_test = model.predict(test_X)\n",
    "# 将测试集的预测结果转换为原始数据的范围\n",
    "ypred_test = inscaled(ypred_test)\n",
    "# 将测试集的真实标签转换为原始数据的范围\n",
    "y_test = inscaled(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84f61209",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test[0:len(ypred_test)-1]\n",
    "ypred_test = ypred_test[1:len(ypred_test),0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cbde8d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "RMSE 0.553 \n",
      "MAE 0.236 \n",
      "R2 0.993 \n",
      "MAPE 6.497 \n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "# 计算测试集的均方根误差（RMSE）\n",
    "testScore = math.sqrt(mean_squared_error(y_test, ypred_test))\n",
    "print('RMSE %.3f ' %(testScore))\n",
    "# 计算测试集的平均绝对误差（MAE）\n",
    "testScore = mean_absolute_error(y_test, ypred_test)\n",
    "print('MAE %.3f ' %(testScore))\n",
    "# 计算测试集的R平方值（R2）\n",
    "testScore = r2_score(y_test, ypred_test)\n",
    "print('R2 %.3f ' %(testScore))\n",
    "testScore = wMAPE(y_test, ypred_test)\n",
    "print('MAPE %.3f ' %(testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d0954a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出测试集预测结果与真实值的对比折线图\n",
    "plt.figure(figsize=(12, 3))\n",
    "# 创建一个图形窗口，设置图形窗口的大小为宽度 12，高度 3\n",
    "plt.plot(y_test[2414:2700], label='True', color='r', linewidth=1) # 绘制 y_test 数据的折线图，并设置标签为 'Actual'\n",
    "plt.plot(ypred_test[2414:2700], label='Predicted', color='b' , linewidth=1, linestyle=\"--\") # 绘制 y_pred 数据的折线图，并设置标签为 'Predicted'\n",
    "plt.title('LSTM-CNN Prediction', size=10)  # 设置图形的标题为'DBO-LSTM Prediction'，字体大小为10\n",
    "plt.ylabel('PV/MW', fontsize=10)  # 设置y轴标签为'PV（kWh）'，字体大小为10\n",
    "plt.xlabel('Sampling points', fontsize=10)  # 设置x轴标签为'Sampling points'，字体大小为10\n",
    "plt.legend()\n",
    "plt.savefig(\"LSTM-CNN.png\")\n",
    "# 显示图例\n",
    "plt.show()\n",
    "\n",
    "# 显示图形窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dde0cfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd\n",
    "import xlwt  #对xls文件进行改写\n",
    "from xlutils.copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1367737b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新建表格\n",
    "def excel_create(path, sheet_name):\n",
    "    workbook = xlwt.Workbook(encoding = 'utf-8',style_compression = 0)  #相当于创建了一个EXCEL文件，style_compression:表示是否压缩，不常用)\n",
    "    workbook.add_sheet(sheet_name)  # 在工作簿中新建一个表格\n",
    "    workbook.save(path)  # 保存工作簿\n",
    "    \n",
    "# 写入表头\n",
    "def excel_write_title(path, title):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格，即index=0\n",
    "    for j in range(0, len(title)):\n",
    "        new_worksheet.write(0, j, str(title[j]))  # 在表格中第一行（row=0)写入标题\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格按列写入一维数组（列表）\n",
    "def excel_write_array(path, value, column):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        new_worksheet.write(i+1, column, float(value[i]))# 从第二行开始(row=i+1)，按对应的列(column)向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格写入二维数组（列表）\n",
    "def excel_write_array2(path, value):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        for j in range(len(value[0])):\n",
    "            new_worksheet.write(i+1, j, float(value[i][j]))# 从第二行开始(row=i+1)，按对应的行和列向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43169c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "path       = r'step2-TCN-BiLSTM.xls' \n",
    "sheet_name = 'NOx'\n",
    "title      = ['TCN-BiLSTM','y_test'] #表头\n",
    " \n",
    "excel_create(path, sheet_name) #新建表格\n",
    "excel_write_title(path, title) #写入表头\n",
    "#写入需要的一维数组(lat,lon,nox是之前已有的一维数组）\n",
    "excel_write_array(path, ypred_test,0)\n",
    "excel_write_array(path, y_test,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a18f6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "131a11f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1b4160",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f282a77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4591fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8999cb9c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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